{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d71bc8d8-8acb-4f71-a2c0-c1bc965e34a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.dont_write_bytecode = True\n",
    "\n",
    "import os\n",
    "\n",
    "module_path = os.path.abspath(os.path.join('.'))\n",
    "if module_path not in sys.path:\n",
    "    sys.path.append(module_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2d95e5a1-fcec-4566-96d9-2e717d53414e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from _lib import knn, write_lance, _get_nyt_vectors\n",
    "\n",
    "import numpy as np\n",
    "import tempfile\n",
    "import random\n",
    "import lance\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "from tqdm.auto import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9fe600ec-7018-4605-a860-d18042d4eb5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def run_test(\n",
    "    data,\n",
    "    query,\n",
    "    metric,\n",
    "    num_partitions=256,\n",
    "    num_sub_vectors=8,\n",
    "    nprobes_list=[1, 2, 5, 10, 16],\n",
    "    refine_factor_list=[1, 2, 5, 10, 20],\n",
    "):\n",
    "    in_sample = data[random.sample(range(data.shape[0]), 1000), :]\n",
    "    # ground truth\n",
    "    print(\"generating gt\")\n",
    "\n",
    "    gt = knn(query, data, metric, 10)\n",
    "    gt_in_sample = knn(in_sample, data, metric, 10)\n",
    "\n",
    "    print(\"generated gt\")\n",
    "    \n",
    "    with tempfile.TemporaryDirectory() as d:\n",
    "        write_lance(d, data)\n",
    "        ds = lance.dataset(d)\n",
    "\n",
    "        for q, target in zip(tqdm(in_sample, desc=\"checking brute force\"), gt_in_sample):\n",
    "            res = ds.to_table(nearest={\n",
    "                \"column\": \"vec\",\n",
    "                \"q\": q,\n",
    "                \"k\": 10,\n",
    "                \"metric\": metric,\n",
    "            }, columns=[\"id\"])\n",
    "            assert len(np.intersect1d(res[\"id\"].to_numpy(), target)) == 10\n",
    "    \n",
    "        ds = ds.create_index(\"vec\", \"IVF_PQ\", metric=metric, num_partitions=num_partitions, num_sub_vectors=num_sub_vectors)\n",
    "    \n",
    "        recall_data = []\n",
    "        for nprobes in nprobes_list:\n",
    "            for refine_factor in refine_factor_list:\n",
    "                hits = 0\n",
    "                # check that brute force impl is correct\n",
    "                for q, target in zip(tqdm(query, desc=f\"out of sample, nprobes={nprobes}, refine={refine_factor}\"), gt):\n",
    "                    res = ds.to_table(nearest={\n",
    "                        \"column\": \"vec\",\n",
    "                        \"q\": q,\n",
    "                        \"k\": 10,\n",
    "                        \"nprobes\": nprobes,\n",
    "                        \"refine_factor\": refine_factor,\n",
    "                    }, columns=[\"id\"])[\"id\"].to_numpy()\n",
    "                    hits += len(np.intersect1d(res, target))\n",
    "                recall_data.append([\n",
    "                    \"out_of_sample\",\n",
    "                    nprobes,\n",
    "                    refine_factor,\n",
    "                    hits / 10 / len(gt),\n",
    "                ])\n",
    "                # check that brute force impl is correct\n",
    "                for q, target in zip(tqdm(in_sample, desc=f\"in sample nprobes={nprobes}, refine={refine_factor}\"), gt_in_sample):\n",
    "                    res = ds.to_table(nearest={\n",
    "                        \"column\": \"vec\",\n",
    "                        \"q\": q,\n",
    "                        \"k\": 10,\n",
    "                        \"nprobes\": nprobes,\n",
    "                        \"refine_factor\": refine_factor,\n",
    "                    }, columns=[\"id\"])[\"id\"].to_numpy()\n",
    "                    hits += len(np.intersect1d(res, target))\n",
    "                recall_data.append([\n",
    "                    \"in_sample\",\n",
    "                    nprobes,\n",
    "                    refine_factor,\n",
    "                    hits / 10 / len(gt_in_sample),\n",
    "                ])\n",
    "    return recall_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a68e21fb-7d4a-49af-9242-cd052874a98a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_plot(recall_data):\n",
    "    df = pd.DataFrame(recall_data, columns=[\"case\", \"nprobes\", \"refine_factor\", \"recall\"])\n",
    "    \n",
    "    num_cases = len(df[\"case\"].unique())\n",
    "    (fig, axs) = plt.subplots(1, 2, figsize=(16, 8))\n",
    "    \n",
    "    for case, ax in zip(df[\"case\"].unique(), axs):\n",
    "        current_case = df[df[\"case\"] == case]\n",
    "        sns.heatmap(\n",
    "            current_case.drop(columns=[\"case\"]).set_index([\"nprobes\", \"refine_factor\"])[\"recall\"].unstack(),\n",
    "            annot=True,\n",
    "            ax=ax,\n",
    "        ).set(title=f\"Recall -- {case}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9eee7907-3494-47a5-ac8a-a4bff2ab7055",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1600x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# test randomly generated data\n",
    "data = np.random.standard_normal((100000, 64))\n",
    "query = np.random.standard_normal((1000, 64))\n",
    "\n",
    "recall_data = run_test(\n",
    "    data,\n",
    "    query,\n",
    "    \"L2\",\n",
    ")\n",
    "\n",
    "make_plot(recall_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0bf731d5-7a4d-46d4-8405-4abadb5d754d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "generating gt\n",
      "generated gt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fcdd5f7276cc4bf29f7b81a0d0de895b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "checking brute force:   0%|          | 0/1000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2024-03-01T06:14:10Z WARN  lance_linalg::kmeans] KMeans: more than 10% of clusters are empty: 221 of 256.\n",
      "    Help: this could mean your dataset is too small to have a meaningful index (less than 5000 vectors) or has many duplicate vectors.\n",
      "[2024-03-01T06:14:10Z WARN  lance_linalg::kmeans] KMeans: more than 10% of clusters are empty: 197 of 256.\n",
      "    Help: this could mean your dataset is too small to have a meaningful index (less than 5000 vectors) or has many duplicate vectors.\n",
      "[2024-03-01T06:14:10Z WARN  lance_linalg::kmeans] KMeans: more than 10% of clusters are empty: 104 of 256.\n",
      "    Help: this could mean your dataset is too small to have a meaningful index (less than 5000 vectors) or has many duplicate vectors.\n",
      "[2024-03-01T06:14:10Z WARN  lance_linalg::kmeans] KMeans: more than 10% of clusters are empty: 29 of 256.\n",
      "    Help: this could mean your dataset is too small to have a meaningful index (less than 5000 vectors) or has many duplicate vectors.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c6406ecb22a84340908f3f4bdd201bdf",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "out of sample, nprobes=1, refine=1:   0%|          | 0/1000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fc4d399fa993434bba94a07ae16d930d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "in sample nprobes=1, refine=1:   0%|          | 0/1000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0336a86222f049b0bafb1c233270e7ba",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "out of sample, nprobes=1, refine=2:   0%|          | 0/1000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2bd45486d46347b7a19ab7b5b1c234d0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "in sample nprobes=1, refine=2:   0%|          | 0/1000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "dc1f8f32d3a64ed5981e6c02f695b6e7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "out of sample, nprobes=1, refine=5:   0%|          | 0/1000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "aa4ec62435274d278ae3c3624311b5d2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "in sample nprobes=1, refine=5:   0%|          | 0/1000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f294dfc4126542c789adb06f3bdd71bb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "out of sample, nprobes=1, refine=10:   0%|          | 0/1000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "398b9b677caf44778f25952c228f4435",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "in sample nprobes=1, refine=10:   0%|          | 0/1000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "524783c9747e4ed7b948e8b515c1e109",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "out of sample, nprobes=1, refine=20:   0%|          | 0/1000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "afd0a179b02848ada38d8445e83a2c47",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "in sample nprobes=1, refine=20:   0%|          | 0/1000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4d713120c5ec4065bd0c49ddd452bd5f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "out of sample, nprobes=2, refine=1:   0%|          | 0/1000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "20a7da8e2f5d4976b1348b2419428854",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "in sample nprobes=2, refine=1:   0%|          | 0/1000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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TW7T4h5kW9R0cHDRoRG+t3rpchwK266/D6zRpxsfy9vVMjq681Fq0a6z1f/9PewO26rs18xLMqI1b1dfin2brrxO/668Tv2vuiumx6ncf2FG/bFuuv89teVTn1YR/v0gNmrV7T2v+XqW//bdo6eq5CebR996vp4U/ztS24+u07fg6ff39tFj1uw3soJ/+XKZdZzeZ6/zX7xdIfRgETKQmTepp0sSRGvPJFL1WrpYOHDyq1b8tlZdXpjjrl3+9jJZ+M0MLFnynMmVr6uef1+mHlfNUpEhBc52Tp86qT5+PVPLVaqryVkP5B1zQmtXfytPTQ5KUObOP1q1dptNn/FWhUl3Vefd9FSlcUPPnTU2OLr80Gr73jj4ZN0zjx03Xm5Xq6/Dh4/rhxwXy9PKIs37ZcqU0d8HnWrJohapUrKfffv1dS5bN1CuF81vU27D+DxXM87r56NiubzL05uVRp0ENDRszQNMmzlbdqi117MhJLVrxlTJ5usdZ/9XXSuiLr8fp+6U/6t23Wmj96i2atXiKChTKa66TI1c2ff/bfJ05dU4t6nfSO1WaavrkOYqIiJAk+fh6ycfXS2NHfq5alZvog14jVaVaBX32xchk6bM11K5fXYNH99WMSXP1XvU2OnHklOYsnyaPeO5zydeKadLsMfrh25/VqFprbVzzh6Yvmqj8hfKY62TPlVVLf5mjc6cD5Negqxq81VIzp8xTRMR9SVLgpSBVLlrb4pg+frZu37qtPzf9lSz9fhnUql9dg0b30czJc9XkbT+dOHJKs5dNjf/elymmCbM+1v++/UVNqvtp05qtmrZwgvI9du+rFH3H4viozxjFxMTo9982J1e3rC8mxnoHgFieNaO6ubrq3NnzGvbRWF25EhRnndcrvKOs2Uuaj5q1mkuSfvjh1xfWj5fds+ZRV1cXBZy7oNEjJyowMDjOOl/MGKs3q1ZS104DVbFcHW3atE0//rJYmTP7vMiuvPQeZNT+mjbxa9Wr2lLHj5zSwhUz4s2o5SqW1i+r1ur9Bp3VuFZbXbkUpEUrv5KPr5ckycXVRUWKF9KXk+eqXrWW6u43ULnz5dTXS6YmY69ePg9z0leT5/2bk05r9rIv4s1Jr1V4Vav/t17tG3XX+3U6KvBSsL5ePk3e/95nSQo4e16fDpukhm+2VOt6nXXpwhXNWT5N7pkyJlOvXj4161fTB6N6a9bkeWpWo61OHDmlWd99Hu99LlPhVa358Xd1eK+nWr3bWYGXgzRr2VTL+3zmgsYOm6xGb7aSX/2uunzhimYt/yL13WcyaYJMhmEY1m5EUnNwypps1/pr2y/6+58D6tP3I0mSyWSS/9m/NeOrBZowcUas+t8unak0bm6q39DPXLb9z1+0/8AR9eg5JM5rpEuXVtevnlCNms20afM2dezwvkaP+kDZcpTSwz++okULaf/ejSr4SkWdOeOf9B2Nq11Orslynfj8vnml9u09pEEDRkt6cO8Pn/hTc2Z9o6lTZseqP2/RF0rj5qrmTTqby9ZvWqnDh46qf58Rkh7MBMyQIb1ateiWPJ14Su4u6ZLtWqvWLdbBfUc0ash4SQ/u6/aDa7V4zjLNmrYgVv1pcz+Tm5urOrbsYy77Ye0iHTt8Uh8NfDAz4Is5nykqMlIDug9/6nbUrlddU2Z+qqI5Kig6Onk2d3W2c0yW60jSsjXzdXj/UX0ydJKkB/d58/5ftGTu95o7fXGs+lO+/lSubq7q1qr/o3OsnqdjR05p9AefSZImz/5EUVFRGtxj1FO344eN3+jYwRP6qN8nievQM7A3Wff507dr5unwvqMaO2yypAf3fsO+n/TtvBWaN/2bWPUnff2JXN1c1KPVQHPZ0tVzdeLwSX08aEKc1/hi4XilSeumjo17vZhOPIXDQTuT9Xr3di5P1us9zuX1Zla7NpCQ5MykT3rWjPq40yd3atr0uZo2fW6C9SZPGq0671RTocKxVwAkJ2tm0mfNo487cGSLZs5YqFlfLTSXubg460LgAb3frKvWr9tiLt/854/a8Psf+vTjz19EN56Kh0t6q11bkn5Yt0gH9x3V6Mcy6raDa7R4zjLNnrbwPz9vZ2envWe2aPTg8frf97/FWadYqcL68fclqlTiHV25FJiUzX9qLvbJl0fj8t2aeTq875g+HfYoo27c97O+nbcizoz6JDs7O+04uUGfDp2on1esibNOmrRptPvMJrVv3EO7/vwnSdv/tKydR5eunqvD+49p3GN5dP3en/TdvBWa/2XsPPokOzs7bTuxXuOGTdYv8d5nN+04vVGdGvfSrm3Wuc+SdDBwR7Jej0yaMGYCJoKjo6NefbW4Nm7601xmGIY2btqm118vHednXi9X2qK+JK3/fUu89R0dHdWp4/sKCwvXgYNHJEnOzk66fz9Sj4/f3r17T5JUsULZRPUppXB0dFTJUkW1ZfN2c5lhGPpj8196rWypOD9TtmwpbdlsOdtp08Y/Y9WvVLmcTp7bpd1712vy1NFy98iY5O1/WTk6OqhoiVe0/Y9d5jLDMLT9j10q9VrcS1teLVPcor4k/bl5h0qVeVDfZDLprbcr6dyZ81r4/QztPrZRq9Yt1tu130ywLenSp9Otm7eTbQAwOTk6OqhIiULasfVvc5lhGNqx9W+VLBP3cp8SZYppx9bdFmXbtuw01zeZTKrydkX5nzmvOcunaduRtVq2Zr6q1a4S1+kkSYWLF1LhYgW18tufkqBXKYODo4MKFy+onX9a3vudW/9WifjufemiFn9WkvTX5p3x1s/k5aE3qlfUqm9/SbqGA8AzeJ6M+jzXeL9lIy1cZL1ftqztefLof3FwcJCDg4Pu/bta4qF7d+/p9fJlEtXelOxhRv3riYz6VwIZ9Umubi5ydHBQWNiNeOukS5dWMTExuhl+M9FtTokcHR1UuHgh7fjzUeb8r5z0JBdXFzk42Cs8nvvs6OigJq0b6Eb4TZ04cipJ2p3SODg66JXiBbXzid8Fdv35t0qUKfpU53hwnx3ivc8Ojg5q/PA+H02d9xlxe6kHAS9cuKD27dsnWCciIkI3btywOJJrcqOnp4ccHBwUHBRqUR4cHCJfH684P+Pr66Wg4BCLsqCg0Fj167xTXWHXTur2zbPq07uTatVuoatXr0uSNm/ZLl9fLw3o31WOjo7KmDGDxn46TJKUObN3UnXvpZYpk7scHBwUEnzVojwkOFTePnHv4+Ht46mQkNA46j+69xs3bFW3zh+owbutNWrEBFWoVE4rVs2Tnd1L/b9KknH/976GhlyzKA8NuSov77iXD3l6e8auH/yofiYvD6VNm0Zde7fT1k1/ya9JN63/bbNmLpqsshXi/kXE3SOjeg3opGWLf0iCXr18MnpklIODg64+cd+uhlyTZ7z3OVOs+/yg/oPlRpm8PJQmbRp17OWnbZt2qGOzXtqweoumLRiv18rH/YtI4/fr6fSJs9r/96Ek6FXK4B7vvb+e4L1/sn5oAvXrNX1Hd27d1obftiRJm1MMI8Z6B/ACPU0elaybSZ/0PBn1WdWvX0sZM6bXosXfJ8n5UqLnyaP/5dat29q9c68+GNxTvr7esrOzU9Nm9fVauVLySaI/u5TIPVPGeDLqtXgz6pMGjeitoMCQWA+vH3JydtLgkX30y6q1unXrdqLbnBIlnFHjXuL+pAHDeyg4KDTWA9Qqb1fU32c3a+/5P9WmS3N1atpLYdfCk6ztKUn8eTT+3wWe1G94d4UEhVgMJErSG29X1M4zG/VPwB9q1bm5ujTrk/ruM5k0QS/1yMa1a9e0aNGiBOuMGzdOGTJksDiMmJT/5Gbzlu0q/VoNVX6jvtat36Lvvp1l3sPl6NGTatehr/r17aKb4ad16cI++Z87r8DAYMWkkHXoL6tVK3/TmtUbdfTISa3+dYOaN+6k0mVKqNIb5azdtBTr4QDqhrVbNH/WUh07fFKzpi3QpvV/6v22jWPVT5s2jeZ9N02nTpzVFxMSXkaDR0wmkyRp09qtWjT7Ox0/fEpzpy/WlvXb1MyvUaz6zi7OqtOopn749ufkbqrNa9jiXf26ar3u/7sXI4CU7WnyqGS7mTQ+7ds219p1m+PdPxDPr0ungTKZTDp2+i8FXTuqzt3a6IcVvyomhfyC+TLq0rut3m1YU938Bsb577ODg4OmzxsvmaQRA8dZoYW2oWOvNqrd4G31aTc41n3evX2P3qvaWu+/20nbNu/U5Dlj493/Dglr37O1atV/W33bDYl1n//evkdNqvmpzbudtX3zTk36+hPuMyw4WPPiP/+c8C+fZ8+e/c9zDB06VP3797coc89UKFHtelqhodcUFRUV60mft7eXAoNC4vxMYGCIfLwtn+L5+HjGqn/nzl2dOeOvM2f8tWv3Xh07sk3t27XQ+AlfSpKWLftRy5b9KG9vT92+fUeGYahv3846e+58Evbw5XX16nVFRUXFevLn5e0Z66n3Q8FBofLy8oyjftx/VpIU4H9BoaHXlCdPTm3dkrx7GVjD9X/v65ObWXt6ZYr1lPuh0ODQ2PW9H9W/fvW6IiMjdeqE5f/PZ06eVelyljPU0qR104LvZ+j2rTvq6tdfUVFRie3SSynsWpiioqKU6Yn7lsnLQ6Hx3uerse7zg/rXzOeMjIzSmZPnLOqcPeWvV8uViHW+mnWrysXVRT99vzoxXUlxrsd7790TvPdP1veMp/6r5UooT/5c+qDzR0nX6JSCh1BIoZIij0rWzaRPep6M+ixy5MiqatUqq3HT2G8QTk2eJ48+Df9z5/VurZZyc3NVunRpFRQUonmLvlDAuQuJbXKKdf1qWDwZ1SPejPpQxx6t1bVPO7V5r2ucyyIfDAB+pqzZMqtVwy6pdhag9F8Z9Vo8n3qgbbf31aFXG3Vs0lMnj56O9f27d+7pvP9Fnfe/qIN7Dmv1jpVq1LKe5k7774cstib+PBr/7wIP+XVrqfa9Wqtz0946dexMrO/fvXNPF/wv6oL/RR3ce0S//PW9Graoq3l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      "text/plain": [
       "<Figure size 1600x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# test randomly generated data -- cosine\n",
    "data = np.random.standard_normal((100000, 64))\n",
    "query = np.random.standard_normal((1000, 64))\n",
    "\n",
    "recall_data = run_test(\n",
    "    data,\n",
    "    query,\n",
    "    \"cosine\",\n",
    ")\n",
    "\n",
    "make_plot(recall_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f8243a51-5222-4446-9696-a031281f17a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading from cache\n",
      "generating gt\n",
      "generated gt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6afedef81f134cefac769b2c2afc5e96",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "checking brute force:   0%|          | 0/1000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "924edcc8518b45259934e74b867b8563",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "out of sample, nprobes=1, refine=1:   0%|          | 0/100 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "97cd2aa87d974a02a4695c726068830a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "in sample nprobes=1, refine=1:   0%|          | 0/1000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "70b6819d2ecb48caa5e78fa79eef1afa",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "out of sample, nprobes=1, refine=2:   0%|          | 0/100 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "496f78313d8448c796ccce13f4ccf019",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "in sample nprobes=1, refine=2:   0%|          | 0/1000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b8bfeb500f3846379e37025263ac528e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "out of sample, nprobes=1, refine=5:   0%|          | 0/100 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4abc75110e3449e382e0c6e6fe1f92e0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "in sample nprobes=1, refine=5:   0%|          | 0/1000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "709f036a4e544323b5ade1649d2ca80d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "out of sample, nprobes=1, refine=10:   0%|          | 0/100 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "48e94c93e0564671978785e102db8a08",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "in sample nprobes=1, refine=10:   0%|          | 0/1000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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AAABZ6546CuvWrat58+aZP5tMJqWmpurTTz9VgwYNsqxyAAAAQEaISQEAALLWPU09/vTTT9WwYUPt2LFDiYmJGjRokA4ePKjo6Gj99ddfWV1HAADwgDMMW9cAORExKQAAuBkxaebd04jC8uXLKzg4WHXq1FHLli0VHx+vF154Qbt371aJEiWyuo4AAABAOsSkAAAAWeueRhRKkoeHh959992srAsAAHhIpbJwNGyEmBQAAPyLmDTz7rmj8NKlS5o1a5YOHz4sSSpbtqy6deumfPnyZVnlAAAAgNshJgUAAMg69zT1eOPGjSpatKg+//xzXbp0SZcuXdLnn3+uYsWKaePGjVldRwAA8IAzDJPNNuRcxKQAAOBmxKSZd08jCvv06aP27dtr6tSpsre3lySlpKSod+/e6tOnj/bv35+llQQAAABuRUwKAACQte6pozAkJESLFy82B2SSZG9vr4EDB2revHlZVrmHRfeendT3jR7y8fXWwQNHNOTtD7R7574M8z/XqomGDuuvwo/563joCb0/YqxWr9wgSXJwcNA77/VXUKN6KlK0sC7HXdaG9Zv1wYixCg+/YC6jRMmiGvnBINWoGSAnR0cdPHhUH384UZv+3Hrf22tL7bq9oC69/6f83vkUfChEn7w7QQd3H84wf1CLBuo9qKcKFvbTqbAz+vzDqdq0ZrN5/6tvdVfjlkHy8/dRUmKSDu87qi/GzNCB3YckSQFPVtFXP31htexOTXro0J4jWdvAB0TDl5qo6ast5eHtqdOHT+jbEbN0fG9IhvmrP1tLL7zZUV6FvBURdl4/fPyt9q3fZd7/8ti+qtumgcUx+zbs1rguH5o/t+jTWpWerqrHyhZTclKyelfsnPUNe8A83iVI5Xs1k6u3h6IPndLW9+Ypas/xDPMXaV5DVd9uo9yFvBQXFqEdoxfo7Nq9Fnk8ShZUwLsd5FfzcZkc7BQbfE7rek5S/LmLkqQ8RXxU7b3/ybdGadk5Oers+n3aOmyurkfF3de2Pugc6zwrp6dfkClPXqWeC9P1H6cr9dQxq3nt/B6TU9NOsi9cQnb5fHX955lK2rAkm2sMQCIm/S/Pdm6mVq++oLzeeXXicJhmDJ+uY3uD//O4ui2e0ltTBmnLis0a0/MjSZK9g706vf2SAhpUk99jfrp6OV57N+3VvI+/VnRE9P1uygOtSLdnVLx3Czn7eCju0CkdfOdrxe4OtZq3UPunVOnzXhZpKdcTtbxIF/Nnv2er67EuQfKoWExO+fLoz6eHKO7gyfvahoeFc5NWcm7ZQXae+ZRyIkRXZ32ulBDr8bhTgyZy6zvEIs1ITFRMx0bmzyaPvHJ96VU5Vqomk1tuJR/ap6uzJin1/Nn72o4HnUNAkBxrNZMpt4dSI04pccU8pZ7LOEaVcy45NWgr+zLVZXJ1kxEbpcSV3yolNC1OdajaUI4BDWXy9JYkpUaeUdKfPyslNOPfzDnBgu0hmrs5WBevXFdpXw8NblJFFfwzXjYj7nqivlh3UGuPnFXstUQV8MiltxtVUt1SBcx5IuKuadKa/forNFzXk5JVOG9ujXqumsoVZDkO3Ll7mnpctWpV8zowNzt8+LAqVaqU6Uo9TFq98Kw+GD1Un338hZ6u20oH9x/Rop9mycvL+v+I1WtU0YzZ4zV/3iI1qNNKS/9YrXnfTdHjT5SSJLnmclHFSuU07tMv1bDu8+ryYl+VLFVM3y6YalHOdz9Ml4ODg55v3lkN6z2vgweOaP4P0+Xj43Xf22wrjVo21JsjX9f0cbP1v0bdFXwwRF9+P155vTyt5q9UrbzGTB2pX77/XR2f6ab1y/7U+DljVOLxYuY8J0NP65N3xqtt/c7q1rK3zp0O15cLJyhv/rQy927fr6AKLSy2n75dojMnzz6ynYQ1mj+pjsO66tdJP2hEs7d1+tBJvTXvPeXJ7241f8mqZdTr8wHauHCNhj/7lnat3KZ+MwbJv3Rhi3z71u/SG9V7mLepr0+w2O/g5KDtSzdr7bcr7lvbHiRFnwtU9RGdtGf8z1rSZJiiD53SM/MHyyWD6+xdrZTqTemj4O83aEnjYTq1YqeenjVAnmUKmfPkKeKjpr+8p9iQc1re5iMtCXpHeyf+opSEJEmSg6uznvlusGQYWt5utJa2GiV7R3s1/PpNyfToDJW/Ww5V6si51ctKWP69ro7tr5SzYcr12vsy5fawfoCjs4yL4Ur4ba5SY3P2j+ObGYbtNuRcxKQZq9Oirrq/97IWTvxeA5v1U9jhMI389n155M/gu+0fPoV81HVYdx3cesAi3dnVWSXKl9APny/QwGf7acwro+Vf3F/vznrvfjbjgVegZU09MeolHRv3ozY9844uHzypwAVD5ORl/X4uSUlxV7W6/GvmbW21Nyz22+dyVvTWozry4ff3u/oPFccnG8i1a29d/+Frxb3dUyknQ5X7vc9kcvfM8Bgj/opierxg3mJfa2+xP/fgD2XnW0BXPn5XcW/1VGpkuHKPGCc5u9zn1jy47MsGyumZTkr682dd+2qYUiNOyaXjYClXBn/TdvZy6TREJg9vJfw4Sdemvq2EP2bJuHzJnMW4HK3EtQt17athujbrPaWcOCTndgNl8vLPplY9eFYcPK1xq/bp1afK6vueQSrt66ne3/2p6PjrVvMnpaTqtW//1LmYeH3WpqZ+6d1Yw5sHyCePqzlP3LVEdf16nRzsTfqiYx399FpjDXymotxdnLKrWQ8EYtLMu+OOwn379pm3N954Q/369dPYsWO1adMmbdq0SWPHjtWAAQM0YMCA+1nfB06vvt30zdwf9P38nxR8NFRv9h+ua9eu638vtbGa/9VeXbR29Z/64vNZOhYcqo8/nKR9ew/p5VdelCRdjruiNq266deflykkJEw7t+/VkLfeV+WqFeRfKO1JQb58eVWiZDFNGj9Dhw4e1fHQk/pgxFi5ueXS42VLZ1vbs9uLr7bXT/N/05IFS3U8+IQ+GvSZrl9LUKsOza3m79iznf5et1XzvvxOYcdO6stPZ+rw/mB16Hbj32b5z6u09c8dOnvqnI4fDdO4EZ8rj3tulXqihCQpOSlZFyOjzVvspVjVb1JXSxYszZY220KTl1tow4LV+nPROp0LOaOv352uxGsJeqpdQ6v5G3Vvpv0bdmvZjF91PvSsfhq/QCcOhimoS1OLfEmJyYqNjDFvV+PiLfb/PGGhVsz6XWeOnrpvbXuQlOvZVMHfrVPIDxsVe+ycNg+Zo+RrCSrVoZ7V/GV7NNbZ9ft0cNofig05p92fLVb0gRN6otsz5jxVB7fV2bV7tfOjBYo+eFKXT17Q6VW7dP1i2mhBn+qllLuwtzYNmKGYI2cUc+SM/uw/XV6ViqlAnbLZ0u4HkVP9VkravELJ29YoNeK0EhZ9KSMxQY6Bz1jNn3r6mBKWzFHy7j+llKRsri0AYtI70/LlVlr5/QqtWbRap4+d1tShU5RwLUFB7a1/t0mSnZ2dBn7+lr4fP1/hp8It9l29fFUjOr2nv37fpLPHzyp491FNf2+aSlYsJa+C3ve7OQ+sYq810+lv1+rMgg26EnxW+9+epZRriSrcsX7GBxmGEiJjzVtiZKzF7rOLNylk/E+K2sjU+Zu5tGirhNV/KHHdcqWeOamr08dLCdfl1PDZDI8xJBkx0Te22BudV3YFCsmhTDldnTFBKaFHlXrutK7OmCCTk7Oc6liPe3MCx8CmSt69Tsl7N8qIOqfEpXNkJCXIsbL1GNWhcj2Z/s/efYdHUa1xHP/tpieQ3ulNeu8IYkFAkaIIiAoICApii4KiCIJ6IyiCBcWGBUUQReygIqBIk957ILT0HlJ39/4RDSzZILAhS5Lv5z7zXHP2zOw7Y9x9c+Y9Zzy8lLN4lswnDsqSmiBz9D6Z487m9KaDW2U6vF2W5FhZkmKUt2qxlJstp6p1S+u0rjrz1x/QHS1rqV+LmqoT5K1JvVrJ3cVJS7cdtdl/6bYopWXnatbATmpZLVBVfL3UpkaQ6of6Fvb5aO1+hXp7aFqftmpaxV9V/LzUqU6oqvlXKp2TQrlx0VOPW7RoIYPBIMs5w6QTJkwo0u/uu+/WoEGDirSXRy4uLmreorFmz3y3sM1isWj1qrVq266FzX3atGuhd+Z8ZNW2csUa3dKrW7HvU9m7ssxms1JTC/7YT0pK1sEDRzRocD/t2L5bOTm5GjZ8kOLiErR9265ij1OWObs4q2Gz+pr3xvzCNovFog1/blKzNk1s7tOsdWN99u4iq7Z1qzbohp5din2PO4b0VXpqug7ssT3NtmuPLvLx89a3C3+8zDO5ujm5OKtmkzr64e1vCtssFot2/7VDdVvZHoSu2/IaLfvwe6u2XX9sU6vu7azaGnRorDc3zVNmaob2rtulr15doMyUjJI/iTLA6OKkgGa1tPOtc66bxaLTa3YrqLXthCmodV3tee9nq7aTq3aoes/WBT8YDKp6UwvtfOdH3fz5BPk3qaGM6HjtfOt7RS/fXPC+bi6SxSJT7tnBLVNOnixmi0La1tfpP3eX7ImWBU7OMlatq9zfvjrbZrHIdGCbjDXrOy6uMshcjhZwxtWNnPS/Obs4q07TuvpqzuLCNovFou1rtql+qwbF7jfosbuUmpCq3xb9qkbtGv/n+3h5e8psNiszrWJ+nxtcnOTTrJYOv/Ht2UaLRQl/7JJvm3rF7ufk5a4bNr0hg9Go1J1R2v+/RcrYf6IUIi7DnJ3lVKe+sr9ZcLbNYlHejs1yvqaRcorZzeDuIe+5C2UwGJV/5ICyFnwg8/GjBS+6uBT8f26u1TGVlyfnhk2Vu6J85vsXZHSSMayW8v46N7e3yHR0t4xVbOeoTte0kvnEIbn2HCbna1rLciZN+bvXKW/t97ZLrAwGOTVsL7m4yXTC9jIv5V2eyay9p1M04tqzn8dGg0Hta4Vox4lEm/usOnBazaoEKPLnrVp14JT8PN10S5NqGt6pgZyMBTnY6gOn1LFOiJ78ap02H0tQcGUPDWxTW/1b1S6V87pakJPa76IHCqOioq5kHGVSQICfnJ2dFR+fYNUeH5egetfY/o8xOCRQ8XHW/ePiEhQcYnvKsJubq6ZMfVJLvvpBGelnK7Du6DNM8xe8raOntspsNishPlGD7hip1JTyuc6Yn7+vnJ2dlRRvPc0vMT5JNetWt7lPYHCAzf4BwQFWbV1u7qSX506Vu4e7EmIT9eCgx5SSZH1n91/97r5N61ZtVNzpeDvO5upV2a+ynJydlJqQYtWeGp+qsDq2pwb4BPkqLSH1vP4p8jlnSvjO1Vu1edl6xR+PU3CNUN05/m49+fEkTbvjGVnM5pI+jauem39lGZ2dlHXedcuKT5VPnTCb+3gE+Sor3vq/76yENHkE+Ra8Hugtl0oeavrQbdo64ytt/t9CVbm+uW744FEtG/A/xa7fp/jNh5R/Jkdtnr1LmyO/lMFgUOtnBsno7CSPEN8rcapXPYOXtwxOTjKfMz1GkizpKXIKqVrMXgAciZz0v3n7e8vJ2Ukp532fpySkqGod259tDds2UrdB3fVYz0dsvn4+FzcXDZ04XH9++4eyMrLsDblMcvX3ltHZSTnnVQTmxKfKq164zX0yDp/WjsfeVfqeaDl7e6r22F7q9MNU/XHdeGWfZjmL4hgq+xR8X6dYXyNLarKcqtj+W8B0Mlpn5kyX6dgRGTy95NZ3kLxfekupjw2XJSle5pPRMsXHyOPeUTozd6YsOdlyu22AjIHBMvpVzPXcDJ6VZTA6yZJp/TttyUiVMcB2jmr0DZahZqDyd61V9sJXZPAPkVvP+ySjk/L+PFt8YAiqKo/hz0vOLlJutnIWz5Yl4dQVPJurV/KZHJksFgVUsp7iHuDlpqPFrBt+MjlTf6fE6dam1fXW4M46npSh//28Vfkmix7sWjAz6ERyphZvOqJ7O9TT/dc20K7TyZqxfJtcnIzq07zmlT4tlCMXPVBYo0aNKxmHJCkzM1NffvmlDh06pLCwMA0ePFgBAQEX3CcnJ0c5Odb3kCwWswyGy1p+8ari7OysDz95XQaDQU8+PsXqtRkzpyg+IUm39bhb2dnZunfYAH2+6F3dfH1/xcaWz0GsK+Xvv7borpvuk6+/r+64t7dmvPeChtw6SsnnJdfBYUHqeH07PTV6smMCLcM2fP9X4T+f2B+t43uP6dU/31bDDo21Zy3TakrEP3cSjy/foj3vL5MkJe2OVlCbeqo/5CbFrt+nnKR0rXrgDXWIHK6GI7rLYrYo6tt1StgRVSEHbAGUTVc6J72cfFSynZOaLCY5GZyK2ePq4eHlocdnRWjOU28qPfm/bzo7OTtpwttPyyDpnWfnXPkAy5GUTQeVsulsFVXy3wfUdc2rqj70Jh2YvvgCe+JSmQ7skenAnsKf8/fvkvfrn8qte29lL5wnmUzKnDFZnmMnyPfTH2QxmZS/Y7PytqyXREXSRTMYZMlMU+6PHxZUEMYcVW5lf7l06GU1UGhJPK2s95+Vwc1DTg3bya3PA8qa/2KFHSy8VGaLRf5ebnquV2s5GQ1qFOanuPQsfbLuQOFAodliUaNwPz1yY1NJUoMwPx2OS9NXm48wUIhLcllPPZakw4cPa/bs2YULSDdq1EiPPvqo6tSpc9HHaNSokdasWSN/f38dP35c1113nZKTk3XNNdfo8OHDeuGFF7R+/XrVqlWr2GNERkZq6tSpVm0erv7ydPvvhM5eiYnJys/PV1CQdTVgUHCg4ooZrIuLTVDQeQ8cCQ4OVFysdZXhv4OEVatV0e29h1pVE3bp2lHde96gOtXbFLZPiJiq62+4VoPuvl1vzHqvJE7vqpKclKL8/Hz5B1nf3QsI8ldinO27rwlxicX0ty7nzj6TreNHT+r40ZPauWW3vl27ULcP7q15b8636tf3rl5KTU7T6uV/lsAZXZ3Sk9NlyjdZVQNKkk+Qj1LjU2zukxqfIu9An/P6+xapSjxX/PFYpSWmKrhmaIUcKMxJSpc53ySP866bR5CPsuJtV7NmxafII8h6EWmPQG9l/fPvJScpXea8fKUctH5KX+rBkwpud3YK7ak/dmnJtU/Iza+SLCazctPOaNDWtxR1rGLeYLBkpsliMslY2U/nDpUaKvvKnJZc7H4oysI0DziIvTlpSeSjku2c9BrvemrgU/rrR6clpcmUb5Lved/nvoG+So4v+tkWWiNUIdVDNWne2Zuhhn9uQC058q3G3vCAYo4VrFn47yBhUJVgPXfXMxW2mlCScpPSZM43yS3I+vvcLchHOXEpF3UMS75JaTuPyrNW6BWIsPywpKcWfF/7+st0TrvBx69IlWGxTCaZog7KKezsLBnTkQNKf/J+ydNLBmdnWdJSVTnybeUf3l+yJ1BGWM6ky2I2yeBl/TttqOQjS4btHNWSkSKZTVbTjC0JJ2Ws7CsZnQpekySzqWCNQknmmKNyCq8tl3Y9lfvTvCtzMlcxP083ORkMSsywfnBJYmaOAivZfpBOUCV3OTsZC6cZS1KtwMpKyMhWnsksFyejgip7qM55D1KqFVhZv+2rWEsbkJPa77LK7pYvX65GjRpp48aNatasmZo1a6YNGzaocePG+vXXXy/6OPv27VN+fr4kaeLEiQoPD9exY8e0ceNGHTt2TM2aNdOzzz57wWNMnDhRqampVpuHq9/lnNYly8vL0/Ztu3Xd9R0L2wwGg67r2lF/b9xmc59NG7fpuq4drdq63tBJmzZuLfz530HC2nVqqH+fYUpOSrHq7+lR8OFhMVuv+WA2m2U0lv1KSlvy8/K1d8d+te/SprDNYDCoXefW2rHJ9rqMOzbvVrsura3aOlzXVjs2XXgdNoPRKBc3lyLtfe66VT8s/ln5+SYbe5UPprx8Hd11WI06NS1sMxgMatSpmQ5tOWBzn0NbD6hRp2ZWbY07N9OhLcUnWH6h/qrkV1mpcRVzIMacZ1LijiiFdT5n/SeDQWGdGyt+s+31MeM3H7LuLyn8uiaF/c15JiVsP1Jk6rJ37TBlnrC+ESFJOckZyk07o9BrG8k90FvHf91i51mVUaZ8mU8cklO9c36HDQY5XdNc5qMV848EoCwpiZy0JPLRf/c9Pyet533xN9BLUn5evg7vPKRm15598rPBYFCza5tr/5Z9RfqfOHxCD3d7SI/1fKRw2/jrBu1ct1OP9XxECacKvkf+HSQMqxWuyXc/q/SU9FI7p6uRJc+k1B1RCuxyznrZBoMCujS2qhq8IKNBlRtWU05sxcyJLlp+vkyH98u5aauzbQaDXJq1Vv45VYMXZDTKqUZtmZNtrAF3JlOWtFQZw6rIqU595f39V9E+FYHZJPPpKDnVOjfnNMipZmOZT9rOUU0nDsrgF6JzqzAN/mEFy7qYL/B3k8EgOV123VKZ5uJkVMMwX208GlfYZrZYtDEqTs2q2i54al4tQNFJGTKfMyB7LDFDQZXc5eJUMAbQvGqAjiZafy4fS0pXmI/nFTgLlGeX9V/m008/rccff1wvv/xykfannnpKN99c/NPUirNu3TrNnTtXPj4Fdy8qVaqkqVOn6q677rrgfm5ubnJzc7NqK81px++89ZHemjtd27bu0pZNO/Tg2GHy9PTQF599LUma8+4MnT4VqxenzpQkvfvOJ/ru5880dtwI/bJ8le64s5datGyiiEeek1QwSPjR/DfUrHlj3T3wATk5OSn4nwrE5ORU5eXl6e+N25SSkqa35k7Xq9PnKDs7W0OGDVT1GlX16/JVpXbupe2zdxdp2uvPas/2fdq1dY/uHjVQHp7uhQ8WeeHNSYo7naA3/zdXkvTF+1/q/W/maMiDd+nP39aqR79uatS8gV4YP12S5O7prvsfHabVy9coIS5Bvv6+Gjj8DgWHBurX71davXe7zq1VtUYVffO59UM7yqNlH3yvUTMfVtTOwzqy7aB6jLxNbp5u+nPx75Kk0TMfVnJskhbP+FyS9Mu8HzVx0TT1vL+3tq/cova9r1WtpnX00cSCfw9unu7q9+hAbVq2TqnxKQquHqpBE4co7miMdv6xrfB9/cMDVcm3kgLCA2U0GlW9UU1JUuzRGOWcsb7bVh7sfv9ndZn1gBJ2RClh62E1GtVTzh5uOrhotSSp8+sP6MzpZG15+UtJ0p4Pl+uWr55V4wdu0YnftqlW344KaFZbayecvQu7652f1PWdcYpZv08xa/eqyvXNVO3mllp250uFfeoOvE6ph04qOzFdQa3rqd20e7X7/WVKO3y6dC/AVSR31VK53/24TMcPyRx9QC5d+8rg6q68Db9JktzveVzm1ETl/vBpwQ5OzjKGVjv7zz4BMlapJUtOtiwJFfc6snA0HKGkc9LLzUcl2zmpI6cdf/vBUj0683Ed2nlQB7cdUO+RfeXu6a7fviz4bHtsVoQSYxI1f/onysvJU/SBY1b7Z6YVzFr5t93J2UlPzZ2oOk3q6IXh02R0Msr3n3VyM1IylJ+XX3ondxWJmvujmr8xRinbjih16yHVHH2LnD3ddHxhwfd58zfHKDsmWftfWihJqhtxh1I2H1Tm0Vi5eHuq9kO95VE1SMc/P5t7uvh6yaNKoNxCC4ofvOoW3ATMiUspsh5iRZL9/WJ5PTxRpsP7lX9wr9xvu1Nyc1fu7wUPe/N8eKLMSQnK/vx9SZL7gKHKP7BH5piTMnhWknu/u2QMDFHOb2cfUuLSsassaakyJ8TKqXpteYx4WHl/r1H+9k0OOcerQd6Gn+XW5wGZT0fJdPKwXNr3lMHFTXnbC36nXfs8IEt6svJWFuSo+Zt/k0ubm+XaY4jy/v5FRv9QuV7bR3l/Ly88pssNAwueepyaKLm6y7lJJxlrNFTughkOOcerwZAO1+i5b/9WozA/NQn31+cbDyorL199/5kiPGnpRgVX9tAjNxUUcAxsXUeL/j6sGcu3aXDbujqWlKEP/9qnwW3PPmTm3g71dN9HK/XBmr3q3qiadp1M0tdbovRcr9a2Qii3yEntd1kDhXv37tWXX35ZpH3EiBGaPXv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      "text/plain": [
       "<Figure size 1600x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# test NYT -- TF-IDF sparse vectors projected on to 256D dense -- cosine\n",
    "data = _get_nyt_vectors()\n",
    "data = data[np.linalg.norm(data, axis=1) != 0]\n",
    "data = np.unique(data, axis=0)\n",
    "query = np.random.standard_normal((100, 256))\n",
    "\n",
    "recall_data = run_test(\n",
    "    data,\n",
    "    query,\n",
    "    \"cosine\",\n",
    "    num_partitions=256,\n",
    "    num_sub_vectors=32,\n",
    ")\n",
    "\n",
    "make_plot(recall_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8c5fbd36-6f14-4ae4-adcf-97b103331c90",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading from cache\n",
      "generating gt\n",
      "generated gt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "dfc2ca7d16704ff99a653810421a6ade",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "checking brute force:   0%|          | 0/1000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c52e2091dfb94076be940feba1d90bb6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "out of sample, nprobes=1, refine=1:   0%|          | 0/100 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "622af34110514fa4a6cb2151081fe862",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "in sample nprobes=1, refine=1:   0%|          | 0/1000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5897d44f59d8417a9ed99616fd4310ae",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "out of sample, nprobes=1, refine=2:   0%|          | 0/100 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ac20497304ad44eea0dbf19575da5f6b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "in sample nprobes=1, refine=2:   0%|          | 0/1000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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e05R6KVEhrWrZrX926yFte/87Hft5nSzpjLMkmXO7KXTC6/r37S+VdC7hXjU/2wh5tYGOfbNCJ2b/oYR9J7TznS+VeilJBVrVtls/fssh7R06S5Hz18pIvPk4l/+8u3a+NVkpcYyzJBXo0lCnZi1X1OxVurjvuPb3nizLpSQFt3zabv0LWw7q8NCvFf3z3zKSktO9r5FiUXJ0nPVIiT1/r7oA5GjEpLenTseG+mv2cq39cZUiD5zQd+9NUdKlJFV78al0rzFkKD76nPU4H3PO5vyGn/7U7+PmavdfLOm+yr9jU539frHOzlmuxAPHdOK9z2W5lCi/5s/Yre/bPExmnzyK6PKhLm7creQTp5Wwfocu7z5irXPhj42KGv2N4pc4PovwfuIW9ryS1yxS8t9LZDl1VJdnjZORlCiXJ+xPNrlUD5fJw1OXPh+i1IO7ZJyJUur+7bIcv7Yqxi38RVnOxujyjNGyHNl7pc7uTTJi7K/kyglcqjVQyqYVStnyh4zoE0r6daqM5CS5PFbbbn3nx56SKVceJc4eLcuxfTLiYmSJ2C1L1FFrHadCJZSy51+l7t8sIy5Gqbs2KPXgNpkLFM+aTt2HnCuHK2XbaqXuWCPjzEklLZkpIzlJzmVq2q9ftqZMuTyUOP8zWU4ckBF/Rpbj+2REH/uvgovMJSop6Y8fr5THnVby3z/LOHtazhXS/72PzDV8+HB5e3vbHMOHD09TLyYmRqmpqQoKCrIpDwoKUmRkpN17Hzp0SHPmzFFqaqp+//13DRgwQKNHj9YHH3xw1/dMz23vUVihQgWZTCab7LXevXunqfe///1PLVq0uKNGZFfOLs4qWra45n0+x1pmGIa2r9mqkhVL2b2mRMVS+vVL23TULas3qUrdqpIkF9crKaTJidc+jBqGoeSkZJWqXFrLZy+VJLm6u+qNcW/pywFfKC46LjO7lS2YXcwqUKaIVn5+bSwNw9D+v3aocMWHHdiyB4vJxSzfckW0Z/yCa4WGoag/dyhvpYyNc8XhbXVq+Rad/nOnHnmzacYams2ZXMzyKldEh8bNv1ZoGDqzert8KpfI0L1Lj2iv6GWbdWb1DhV78/mMNfQBYHJxlme5ojo27rqlLoahuD+3yTODY52raLBCt3whS2Kyzv+7T4eHfavEEzEZbHH2YeGlIsgixKS3ZnYx66EyRbX48/nWMsMwtOev7SpaMf3fdW653fXBmgkyOZl0bOdh/TzyO53afzwLWpw9mVyclatMcUVf91lAhqELf21R7ool7V7jFRaqi5v3qMDQV+X5TKhSz8QrbsEfip40V7JYsqjl2ZDZWU4PPazEhbOvlRmGUvZslrloabuXOJerqpRDu+X+v25yLl9NxvlzSv5npZIW/SAZlmt1dm1Urs7vyfxwORlxMUr641clr0l/O5cHmtksp/xFlLzmus+rhqHUQzvkVNB+7G8uWVGW4/vl2qCdnEtWlnExXinb/1LymgXWtH/LsX1yrlRHprzBMs5EyinoIZkfKqXExV9nRa/uP05mOQUXVsr6364rNGSJ2CWn/MXsXmIuXkGWkwflGvayzMUryLh4Xim71ytlw+9XxtlklsnJLKXYfqltpCTLqUDO+nzsyJi0X79+6tWrl02Zm1valaF3w2KxKDAwUJMnT5bZbFalSpV04sQJffzxxxo0aFCmPOOq254oPHz4JmvlcyhPXy+Znc06FxNnUx4XE6cCxQrYvcYnwEdxN9Q/FxMnnwBfSdKJg8cVffy0XurTWl/0m6DES4lq2KGx/PMHyDfQ13pN24EdtXfjHv2zNOv2JLyfePw39jd+030h+pwCi+V3UKsePG5+nnJyNutytO04X46Ol2fxux/nQk2qyrdsES2rn7OWZqbH1c9LTs5mJd0wzonR5+TxsP3fJbcjuGk1eZUrorXh72W0iQ8MFz9PmeyMdVL0OXkXv/uxjt+0X3vfmKBLB07KNchXD73VXOV/HqqNtXopNeFyRpsN4DrEpLeW5784Kf6GmDM+Ok5B6cRJUYdO6uveE3ViT4RyeeZWWKfGemfuBxpat5fiImPtXpPTmX29ZHI2KyXGdluKlJg4uRUraPcal4eC5VGwnOLmr9KRdkPkVjif8r//mkzOZp0eN9vuNZBMebxkMptlnI+zKTfiz8ocXMjuNU4B+eSUt4KS16/QxfH95RRYQO6tuklms5J+nWWt41qroZKWzVPiwtkyh5SQe4vXpJRkJa9bdq+7dd8x5faSycks44JtnGQknJOTv/3fHU6+gTIVCVDKtr90edZHMvkFya1Be8nJWcl/zJWkK5OGbrmUq9voKxPiTk5KXv6DUrf/dc/7dD8y5fK8Ms4X423KjYvxcvLLZ/8a7wA5PfSIUnet0+W5Y+XkEyjXZ16RzGal/L1ASr6s1BMH5FKtkZLOnJJx8ZzMj4TKKX8xGXGns6Jb0JVJwduZGPT395fZbFZUVJRNeVRUlIKDg+1eky9fPrm4uFhf3iZJjzzyiCIjI5WUlHRX90zPbU8UFi5c+I5ufDcSEhL0ww8/6MCBA8qXL59atWqlvHnz3vSaxMTENGu+U41UmU3mdK64v6WmpOrjLsP12sjumrH9O6WmpGrbmq3atPJfmUxXZsYrh1VR2SfK6Z1n33RsY4G7kCu/nyq831qrWwyXJTH9ZZzIGPf8efXIB230z4vDGOcscHbFFut/J+w+qvhN+xX670QFNH5Ckd+tcFzDgAfQvY5J7yYelbJ/THp4034d3nTt5Q0HN+7ToGWfqOb/ntEvY753YMseLCYnk1JizunEuxMki0WXdxyUS3Be+Xd+nonCzGYyyTgfp8vffCoZFlmOHlCST1651m1mnSiUyaTUiP1KnD9dkmQ5dlBO+UPkUqtBjpwovCsmJxkJ8Ur6ZcqVzLZTh5Xk5SeXJxpaJwrNj1aVc9kaSpz7mSynj8scXFiu9VpfeanJ1tUO7kA2YTLJuBivpCVfXcnyjIpQsqevXB6vd2WiUFLS71PkWq+9cr0+RoYlVZaoCKXuWS+noHs/l4M74+rqqkqVKmn58uVq2rSppCsZg8uXL1e3bt3sXlO9enV9++23slgscnK6sovgvn37lC9fPrm6ukrSHd8zPbc9UXijgwcPauzYsdYNpEuXLq033nhDxYrZT5W1p3Tp0lqzZo38/Px07NgxPfnkkzp79qxKlCihgwcP6v3339e6detUpEiRdO8xfPhwDRkyxKbsEa8SKu1jP90/M50/G6/UlFR5+/vYlPv4+6S7HDguOk4+N9T39vdRXPS1byIP7Tiod559U7k9c8vZxVnxsfEaPv9jHdx+5Y2cZZ4op6DCwZqx/Tub+7w9qa/2bNilQS0f/OyhhP/G3tPfdvPiPAHeOp8Dl2LfK4mx52VJSZV7gO04uwd46fLpc+lcdXO+5YrIPcBbYUs+tJY5OZsVULWUirerq7mF20iWnPV2gqTYeFlSUuV6wzi7BXgr8XTcXd3Tq3wRuQX46Iml1/bEcHI2y7daKT3UPlxLCr2c48ZZkpJjz8uwM9auAd5Kusuxtic1/qIuHTop9yJ39u1ddmaw9BgOktGYNDPiUcl+TFrJu7Qe93n07jqWARf+i5O8bog5vQJ8FH+bcZIlJVXHdh5WQEjO+T12p1LPxstISZWzv69NubO/j1Kiz9q9Jvn0WSk5xWaZ8eUDx+US6CeTi7OM5PT3Hc7JjAvxMlJTZfL0sSk3efnKcs7+WBvnYmWkplqXGUuS5dRROXnnlczOUmqKjHOxspyyfdmb5dQxuTxWI9P7kB0YF+NlWFJlymMbJ5k8vG1emmFzzfk4yZJi83YxI/qEnDx9JbNZSk2V6zMvKXnNz0rdceWFCimnj8nkEyCXmo1z5EShcen8lXHObfuCGFNuLxkJ9j9jGQnnJEuqzThbzpy68qISJ7NkSZURF63E2f9v776jo6rWPo7/ZlImvRGSkNB7DUgLoAgKElARBBW4KIiIimAhiogiKN5XbCAWFBvFClZUVFAjIB1pIp3QIkICCekhbWbeP+IdGBPqhEyS+X5cZ63Mnn32POdc7uTJPru8IHl4Sp7eUk6GPPveJ2v6ict5ORVOZclJ4+LiNHz4cLVv314dO3bUzJkzlZOToxEjRkiShg0bpqioKNsah6NHj9Ybb7yhhx56SA888ID27dun5557Tg8++OAFt3mhLmnX46VLl6p58+basGGDoqOjFR0drfXr16tFixb6+eefL7id3bt3q6io+JfhxIkTFRkZqcOHD2vDhg06fPiwoqOj9eST5+70mjhxojIyMuyOJoHlsyhqUWGRDvyZoFZXnt5Vz2AwqNWV0dqzeXep5+zdvFutroy2K2vdtY32llI/NytXmSczFVG3hupHN9TvPxVPM1701hd6JPZBPdrnIdshSfOnvq9ZLrKxibnQrL+3H1TDLi1tZQaDQQ27tNDhM56GwzHWQrPSth1U2FVn/JFjMCjsqpZK3XRp9/n4yh1a2n2Cfu75hO04uXW/Er9ao597PuGSnVfWQrMytx1Uta6n/z3LYFC1ri2VvnHvJbWZ+tt2rer2qNb0mGA7Mrbs19EvV2tNjwkueZ8lyVpYpKxtBxTUtdXpQoNBQVe1UtYl3uvSGH285FUnQgXn2ZEagGPKIicti3z0f+f+OydtG1j6mtWXm7nQrMTtB9TkX3lSky4tdWDzhX3XGYwGRTWtrYzjfI+djbWwSKe2J8j3zNzeYJBfl9bK3byn1HNyN+6UZ90akuH0H7KmepEqTE6lk/BczEWyJO6Te7MrTpcZDHJv2kbmAztLP2X/Thmr299rY3jN4l2PzUWn64TbT102hkfJctJFp2qazbIcPSi3evY5qVv9FrIcKT33N/+1R4aQCLv7bKhWQ5asNOmf3VgNHp52HVySijvLDZfUHVH5WcyyJB2WsU6zMwoNMtZpJsvR/aWf8vc+GYLCJJ3x7zk4XJbs9OIOxDMVFkg5GZLJR251W6ooYWtZXwHKwKBBg/Tyyy9r8uTJatOmjbZu3aolS5bYNiNJTEzUsWOnN1aqVauWli5dqt9//13R0dG2Dd0ef/zxC27zQl3SiMLHH39c48aN0/PPP1+ifMKECbruutJ3+TqXtWvXavbs2QoMLH564efnp2eeeUaDBw8+53mlzQEvzyke3733jcZOf1j7tyUo4Y+9uuGum2Ty8dKyz+MlSQ/MeFipSSf1yYsfSJJ+mPudnln4nPqO6q9Nv/6uq/perfqtGmr247NsbXa+/kplnszQib9PqE7Tuhox5W79/tN6/bFyq6TiUYmljVg8cfSEjv+VXKK8qlr53ve6bfpoHfnzgP7amqCrRvaRp49JGz9fIUkaNH20MpLTtOTF4mkcbh5uCmtUvF6Mu4e7AsODVaN5HRXk5Cn1cPF98/QxqdoZT85DalVXjeZ1dCo9W+lHU8v5CiuGvW//qI6v3qu0Pw7q5Nb9ajSqt9x9TDq0oPg+d3jtPp1KStP254qnJRk83BTQuPg+Gz3c5R0RrMAWdVSUk6ecQ8kqyslT5h77hdGLcvOVn5ZVotyVHJr9vVq9NloZWw8oY0uC6t5zvdx8TPr7n/vc6vX7lZ90Unv/r/jfs8HDTX7/3GeDp5u8IkLk36KOzDl5yj2ULHNOnrJ3299Pc26+CtOySpS7mr/fXqwmr45R9h/7lbklQTVH3SCjj0lJC5ZJkpq8Plb5x07q0HOfSCperN7nf/faw12eNarJt0VdmXPylHeoeAexelPu0MmfNinvyAmZwoNVZ/wgWS0WnVjkOmvvsJkJnKGsc9JLzUcl5+ek/xb/3mINnz5GiX8e0KGtCbp25PUy+Zi09vPlkqTh08coPfmkvnmxeIbK9Q8O1MEt+3TiUJK8A3x13b03KSSqulYviLe16RPoq5CoUAWGhUiSwusXr1mWeSJdmScubaZBZZfy3iLVnD5Op7Yl6NQfe1Xtrn4y+ngp7Yviaas1p49TYVKqkl8q/lvg5Mc/qtqwG1Vjyiilzl8sU91IVR9zq1LnLba1afTxkmed0+uUedQKl1ezejJnZKvwqGuNDDpT/i9fyfvOR2U+tFfmQ3vk2eNmGTy9VLjmJ0mS153jZU1PsU0jLlixWJ7d+8pr0GgV/PqNjGFR8uwzWAW/fmPXpu+EV+TZZ7AKN/4mt7pN5Nn1ep36aKYzLrFCKFz7vUw3j5bl6IHiNe869ZHBw6TCLcU5qefNo2XNTFNhfHFOWvT7z/Lo2EuevYercMMSGUNqyLNrfxWuX2Jrs2jvZnlc3V/WjFRZTvwlY0RdeXS+XoVbljvjEiuEoo1L5Xn93bIkHZLl2EG5t79OBg+TiravkiR5Xn+3rFlpKlxZPH27aOsyuV/RQx49hqhoc7wMweHy6HSDCjef/o421m0hySBrWpIMQWHy7H6bLCePyfxPm66iMuWkY8eOPeu04OXLl5co69y5s9atW3fJbV6oS+oo3LVrlz777LMS5XfddZdmzpx5UW39b929vLw81ahhv3BnVFSUTpyo2L8M1yxepYBqgRoc9x8FVQ/WoZ0H9H/DnrZtcBIaWV2WM0bu7Nm0W68+OF2DHx2q/4y/Q8cOHdWL9zynv/ae3j4+OCxYw5+6q3hK8vE0rfhqmb54jbVh/u2PxevkGxKgXuNukX/1IB3ddVjvD39e2f9scBIUFWq3I2JAeLDG/XD6D4lu9/ZVt3v7av+6nXp78LOSpJrR9XXfgsm2On2fGiZJ2vjFCn326OzyuKwK58i362Sq5q8Wj90ir+qBSt9xWCv/84LyU4oX3/WJqmY3Os07PFi9fnnO9rrJ/Teqyf036vianVox8P9KtI9iSd+slWe1ADV67FaZwoKUueOwNg553rbphndUqN199ooI0ZW/vmB7XW9MX9Ub01cnV+/UhgFTyz3+yuTEN2vkUS1AdR4bJM/qQcrecUjbh/yfCv/57jBFhcp6xr32jAhWu/iXbK9r3X+Tat1/k9LX7NC2AU8Xn1Ojmpq+9ZA8gv1VmJqpjA27tfX6J1SYar9INYCyVVY5aWXPR0uzafFa+YUE6MZxtymgepCO7Dqk14c/Z9sILuRfeZJPoJ+GTrtXAdWDlJuZo8Q/D+ilgZOUlPC3rU70de01/OUxttd3vzFOkrR45uf6fubn5XRlFUvG96vkXi1Q4XFD5R4arLxdB3Twzikq+udvAY/I6na/vwuPpejQ8Mmq8dTdCvnxdRUmpSp17nfFux7/w7tVQ9VfcHrpkMin7pYkpX0RryPjZ5bLdVVERRtXKM8vUKabhhVPOT5yQLmvPWnb4MQYUl2WM6YZW9NOKPe1J2W69V75Tp5dvKPxr4uKdz3+h+XwXp16a6pMN4+Q6YahsqQkKe+z2SrasKy8L6/CMO9YpwLfAHlcc4s8/YJkSTqsvI+eLx6hJskYGCrLmdOMM08q78Pn5dn7DnmPfqG4E3H9j8UbmPyj4Id58rz2NnneMKJ4GnNWmgo3xdvWMHRF5j2/q9DHXx5X9pfBN1CW438p/4tXpH82ODH4h9hNm7dmpSn/ixnyuGawvO6cKmt2mgo3/VK86/E/DCYfeVw9UAa/YCkvR0V7N6lw5VclRxwC52GwWv89Bvj8atWqpRkzZujWW2+1K//ss8/06KOPKjEx8Sxn2jMajWrZsqXc3d21b98+zZs3TwMHDrS9/9tvv+k///mPjhy5uNEvt9S56aLq49LUN/g6OwSX0SHfRYfllzN/fomWCx8D97k8XJ1Uvn+0r4scUK6fd6ZOR79y2mfDucoiJ71c+agkja5720Wfg4t3v+GUs0NwCXV6FTg7BJfgViPE2SG4BIOvt7NDcBk+4+eU6+eRkzrukkYUjho1Svfcc48OHDigLl26SJJWr16tF154QXFxcRfczpQpU+xe+/n52b3+7rvv1LVr10sJEQAAAFVcWeSk5KMAAACnXdKIQqvVqpkzZ2r69Ok6evSoJCkyMlLjx4/Xgw8+aJu+4SyMKCwfjCgsP4woLB+MKCwfjCgsH4wohCuo6DkpIwrLByMKywcjCssHIwrLByMKyw8jCiufix5RWFRUpE8++UT/+c9/NG7cOGVlZUmS/P39yzw4AABQOVSmhaNRNZCTAgCAfyMnddxFD1Nyd3fXfffdp7y8PEnFyRgJGQAAAMoTOSkAAEDZu6T5jB07dtSWLVvKOhYAAFBJWa0Gpx1wXeSkAADgTOSkjrukzUzuv/9+PfLIIzpy5IjatWsnX1/7teqio6PLJDgAAADgbMhJAQAAytYldRQOHjxYkvTggw/aygwGg6xWqwwGg8xmFqoHAADA5UVOCgAAULYuqaPw4MGDZR0HAACoxCzODgAuiZwUAACciZzUcZe0RmGdOnXOeQAAAFRks2bNUt26deXl5aWYmBht2LDhrHV37NihgQMHqm7dujIYDJo5c2aJOk8//bQMBoPd0bRpU7s6eXl5GjNmjKpVqyY/Pz8NHDhQycnJZX1pLoWcFAAAoGxdUkehJO3Zs0djx45Vjx491KNHD40dO1Z79uwpy9gAAEAlYZXBacfFWrhwoeLi4jRlyhRt3rxZrVu3VmxsrI4fP15q/dzcXNWvX1/PP/+8IiIiztpuixYtdOzYMduxatUqu/fHjRun7777Tp9//rlWrFiho0ePasCAARcdP+yRkwIAgP+pTDlpRXVJHYVffvmlWrZsqU2bNql169Zq3bq1Nm/erJYtW+rLL78s6xgBAADOKj8/X5mZmXZHfn7+WevPmDFDo0aN0ogRI9S8eXPNnj1bPj4+mjNnTqn1O3TooJdeekmDBw+WyWQ6a7vu7u6KiIiwHaGhobb3MjIy9P7772vGjBm69tpr1a5dO82dO1dr1qzRunXrLv3iXRw5KQAAQNm6pI7Cxx57TBMnTtTatWs1Y8YMzZgxQ2vWrNETTzyhxx57rKxjBAAAFZzF6rxj2rRpCgwMtDumTZtWapwFBQXatGmTevbsaSszGo3q2bOn1q5d69A92LdvnyIjI1W/fn0NHTpUiYmJtvc2bdqkwsJCu89t2rSpateu7fDnujJyUgAAcCZn5qRVxSV1FB47dkzDhg0rUX777bfr2LFjDgcFAABwoSZOnKiMjAy7Y+LEiaXWTUlJkdlsVnh4uF15eHi4kpKSLjmGmJgYzZs3T0uWLNFbb72lgwcPqmvXrsrKypIkJSUlydPTU0FBQWX6ua6OnBQAAKBsXdKux927d9fKlSvVsGFDu/JVq1apa9euZRIYAADAhTCZTOecElwe+vTpY/s5OjpaMTExqlOnjj777DONHDnSiZFVbeSkAAAAZeuSOgpvuukmTZgwQZs2bVKnTp0kSevWrdPnn3+uZ555Rt9++61dXQAAULVZKskCzqGhoXJzcyux23BycvI5Nyq5WEFBQWrcuLESEhIkSRERESooKFB6errdqMKy/lxXQ04KAADOVFly0orskjoK77//fknSm2++qTfffLPU9yTJYDDIbDY7EB4AAEDZ8fT0VLt27RQfH6/+/ftLkiwWi+Lj4zV27Ngy+5zs7Gzt379fd9xxhySpXbt28vDwUHx8vAYOHCipeLfexMREde7cucw+19WQkwIAAJStS+ootFgsZR0HAACoxKyV6OltXFychg8frvbt26tjx46aOXOmcnJyNGLECEnSsGHDFBUVZdsQpaCgQDt37rT9/Pfff2vr1q3y8/OzTXl99NFH1bdvX9WpU0dHjx7VlClT5ObmpiFDhkiSAgMDNXLkSMXFxSkkJEQBAQF64IEH1LlzZ9tIOFw8clIAAHCmypSTVlSX1FEoSfHx8YqPj9fx48ftkjSDwaD333+/TIIDAAAoa4MGDdKJEyc0efJkJSUlqU2bNlqyZIltg5PExEQZjaf3ezt69KiuuOIK2+uXX35ZL7/8srp166bly5dLko4cOaIhQ4YoNTVV1atX11VXXaV169apevXqtvNeeeUVGY1GDRw4UPn5+YqNjS0xCg4Xj5wUAACg7FxSR+EzzzyjqVOnqn379qp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      "text/plain": [
       "<Figure size 1600x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# test NYT -- TF-IDF sparse vectors projected on to 256D dense -- normlized L2\n",
    "data = _get_nyt_vectors()\n",
    "data = data[np.linalg.norm(data, axis=1) != 0]\n",
    "data = np.unique(data, axis=0)\n",
    "data /= np.linalg.norm(data, axis=1)[:, None]\n",
    "\n",
    "# use the same out of sample query\n",
    "\n",
    "\n",
    "recall_data = run_test(\n",
    "    data,\n",
    "    query,\n",
    "    \"L2\",\n",
    "    num_partitions=512,\n",
    "    num_sub_vectors=32,\n",
    ")\n",
    "\n",
    "make_plot(recall_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "45249d41-24c5-4845-8313-942d4d9853c2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
